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DIAL Publications

2006

  1. Tanu Malik, Randal Burns, Nitesh V. Chawla, and Alexander S. Szalay. “Estimating Query Result Sizes for Proxy Caching in Scientific Database Federations.” Proceedings of the ACM/IEEE Conference on Supercomputing (SC), pp. 102–115, 2006. PDF
  2. Yang Liu, Nitesh V. Chawla, Mary P. Harper, Elizabeth Shriberg, and Andreas Stolcke. “A Study in Machine Learning from Imbalanced Data for Sentence Boundary Detection in Speech.” Journal of Computer Speech and Language, 20(4):468–494, 2006. PDF
  3. Karsten Steinhaeuser, Nitesh V. Chawla, and Peter M. Kogge. “Exploiting Thread-Level Parallelism to Build Decision Trees.” Proceedings of the ECML/PKDD International Workshop on Parallel and Distributed Data Mining, pp. 13–24, 2006. PDF
  4. Dinesh Rajan, Christian Poellabauer, and Nitesh V. Chawla. “Resource Access Pattern Mining for Dynamic Energy Management.” Proceedings of the ECML/PKDD Workshop on Automatic Computing: A New Challenge for Machine Learning, 2006. PDF
  5. Alec Pawling, Nitesh V. Chawla, and Amitabh Chaudhary. “Evaluation of Summarization Schemes for Learning in Streams.” Proceedings of the 17th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 347–358, 2006. PDF
  6. Nitesh V. Chawla and Xiangning Li. “Pricing Based Framework for Benefit Scoring.” Proceedings of the ACM SIGKDD 2nd International Workshop on Utility-Based Data Mining (UBDM), pp. 65–75, 2006. PDF
  7. Danny Roobaert, Grigoris Karakoulas, and Nitesh V. Chawla. ” Information Gain, Correlation and Support Vector Machines.” Feature Extraction: Foundations and Applications, pp. 463–470, 2006. PDF
  8. Jared Sylvester and Nitesh V. Chawla. “Evolutionary Ensemble Creation and Thinning.” Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pp. 5148–5155, 2006. PDF
  9. Nitesh V. Chawla and David A. Cieslak. “Evaluating Probability Estimates from Decision Trees.” Proceedings of the AAAI Workshop on Evaluation Methods for Machine Learning, 2006. PDF
  10. Nitesh V. Chawla. “Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees.” Machine Learning Challenges, pp. 41–55, 2006. PDF
  11. Karsten Steinhaeuser, Nitesh V. Chawla, and Christian Poellabauer. “Towards Learning-Based Sensor Management.” Proceedings of the 1st Workshop on Tackling Computer Systems Problems with Machine Learning (SysML), 2006. PDF
  12. Alec Pawling, Nitesh V. Chawla, and Gregory Madey. “Anomaly Detection in a Mobile Communication Network.” Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), pp. 407–422, 2006. PDF
  13. David A. Cieslak, Douglas L. Thain, and Nitesh V. Chawla. “Troubleshooting Distributed Systems via Data Mining.” Proceedings of the 15th IEEE International Symposium on High Performance Distributed Computing (HPDC), pp. 309–312, 2006. PDF
  14. David A. Cieslak, Nitesh V. Chawla, and Aaron D. Striegel. “Combating Imbalance in Network Intrusion Datasets.” Proceedings of the IEEE International Conference on Granular Computing (GrC), pp. 732–737, 2006. PDF

2005

  1. Alec Pawling, Nitesh V. Chawla, and Amitabh Chaudhary. “Computing Information Gain in Data Streams.” Proceedings of the IEEE ICDM Workshop on Temporal Data Mining: Algorithms, Theory and Applications, pp. 72–81, 2005. PDF
  2. Nitesh V. Chawla. “Teaching Data Mining by Coalescing Theory and Applications.” Proceedings of the 35th Annual ASEE/IEEE Conference on Frontiers in Education (FIE), pp. S1J 17–23, 2005. PDF
  3. Nitesh V. Chawla and Kevin W. Bowyer. “Ensembles in Face Recognition: Tackling the Extremes of High Dimensionality, Temporality, and Variance in Data.” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), vol. 3, pp. 2246–2351. 2005. PDF
  4. Nitesh V. Chawla. “Data Mining for Imbalanced Datasets: An Overview.” Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, pp. 853–867, 2005. PDF
  5. Nitesh V. Chawla, Lawrence O. Hall, and Ajay Joshi. “Wrapper-Based Computation and Evaluation of Sampling Methods for Imbalanced Datasets.” Proceedings of the ACM SIGKDD International Workshop on Utility-Based Data Mining (UBDM), pp. 24–33, 2005. PDF
  6. Jared Sylvester and Nitesh V. Chawla. “Evolutionary Ensembles: Combining Learning Agents Using Genetic Algorithms.” AAAI Workshop on Multi-Agent Systems, pp. 46–51, 2005. PDF
  7. Daniel Mack, Nitesh V. Chawla, and Gregory Madey. “Activity Mining in Open Source Software.” Proceedings of the Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), 2005. PDF
  8. Nitesh V. Chawla and Kevin W. Bowyer. “Random Subspaces and Subsampling for 2-D Face Recognition.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 582–589, 2005. PDF
  9. Nitesh V. Chawla and Kevin W. Bowyer. “Designing Multiple Classifier Systems for Face Recognition.” Proceedings of the 6th International Workshop on Multiple Classifier Systems (MCS), pp. 407–416, 2005. PDF
  10. Nitesh V. Chawla and Grigoris J. Karakoulas. “Learning from Labeled and Unlabeled Data: An Empirical Study Across Techniques and Domains.” Journal of Artificial Intelligence Research (JAIR), vol. 23, 331–366, 2005. arXiv PDF
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